{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:19:05Z","timestamp":1771265945890,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>We introduce an active learning framework for general sequence learning tasks including sequence labeling and generation. Most existing active learning algorithms mainly rely on an uncertainty measure derived from the probabilistic classifier for query sample selection. However, such approaches suffer from two shortcomings in the context of sequence learning including 1) cold start problem and 2) label sampling dilemma. To overcome these shortcomings, we propose a deep-learning-based active learning framework to directly identify query samples from the perspective of adversarial learning.\u00a0 Our approach intends to offer labeling\u00a0 priorities for sequences whose information content are least covered by existing labeled data. We verify our sequence-based active learning approach\u00a0 on two tasks including sequence labeling and sequence generation.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/558","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"4012-4018","source":"Crossref","is-referenced-by-count":26,"title":["Adversarial Active  Learning for Sequences Labeling and Generation"],"prefix":"10.24963","author":[{"given":"Yue","family":"Deng","sequence":"first","affiliation":[{"name":"AI Center, Samsung Research America, Mountain View, CA, USA"}]},{"given":"KaWai","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering,"},{"name":"University of California, San Diego"}]},{"given":"Yilin","family":"Shen","sequence":"additional","affiliation":[{"name":"AI Center, Samsung Research America, Mountain View, CA, USA"}]},{"given":"Hongxia","family":"Jin","sequence":"additional","affiliation":[{"name":"AI Center, Samsung Research America, Mountain View, CA, USA"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:53:53Z","timestamp":1530770033000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/558"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/558","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}